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license: mit |
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--- |
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# ExtAgents |
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<p align="center"> |
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<a href="https://github.com/THUNLP-MT/ExtAgents">🌐 Github</a> | |
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<a href="https://arxiv.org/abs/2505.21471">📖 Paper</a> | |
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<a href="https://huggingface.co/datasets/zhennan1/ExtAgents">🤗 Data</a> |
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</p> |
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## Introduction |
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ExtAgents is a framework for scaling external knowledge input beyond the context length of LLMs via multi-agent collaboration. |
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## Setup |
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```bash |
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conda create -n extagents python=3.10 -y |
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conda activate extagents |
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pip install -r requirements.txt |
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``` |
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## Data |
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You can download the data with the script: |
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```bash |
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bash scripts/download_data.sh |
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``` |
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Or you can download the data manually from one of the following links: |
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- [Google Drive](https://drive.google.com/drive/folders/1FQSojqgF1VdumXxSh1UbIoE6lQ2E_xJn?usp=sharing) |
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- [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/b8aab568cf5c4785b457/) |
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The data should be organized as follows: |
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```bash |
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./ |
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└── data/ |
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├── sampled_hotpot_questions.json |
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├── rag_1000k.jsonl |
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├── longbook_qa_eng.jsonl |
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└── longbook_qa_chn.jsonl |
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``` |
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**Update**: We have uploaded the long context Q&A datasets both before and after enhancement to [Hugging Face](https://huggingface.co/datasets/zhennan1/ExtAgents). `original` refers to the original dataset, `full-enhanced` indicates the fully enhanced dataset, and `partial-enhanced` signifies the partially enhanced dataset, where only samples with a length not exceeding 128k tokens have been augmented. Welcome to download and use! |
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## Usage |
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### Generation |
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We currently support three tasks: RAG, En.QA, Zh.QA. |
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The RAG task is a question answering task, where the input is a question and a context. The question and answer are sampled from the [HotpotQA](https://github.com/hotpotqa/hotpot). The context is a long text, which is the concatenation of documents retrieved from Wikipedia using BM25 embedding. We use [KILT knowledge source](http://dl.fbaipublicfiles.com/KILT/kilt_knowledgesource.json) as our knowledge source. It is based on the [2019/08/01 Wikipedia dump](http://dl.fbaipublicfiles.com/BLINK/enwiki-pages-articles.xml.bz2). We have provided the context in the `data/rag_1000k.jsonl` file. |
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The En.QA and Zh.QA tasks are question answering tasks, where the input is a question and a context. The question, answer and context are from the [InfiniteBench](https://github.com/OpenBMB/InfiniteBench). |
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Here is an example command to generate predictions for RAG task: |
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```bash |
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python main.py \ |
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--task rag \ |
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--output_dir results_rag \ |
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--chunk_length 8000 \ |
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--input_length 128000 \ |
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--api_url "YOUR_API_URL" \ |
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--api_key "YOUR_API_KEY" \ |
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--model "gpt-4o-mini-2024-07-18" \ |
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--num_workers 8 \ |
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> rag.log |
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``` |
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The generated predictions will be saved in the `results_rag` directory. |
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- `--task`: Task, can be `rag`, `en`, `zh`. |
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- `--output_dir`: Directory to save the generated predictions. |
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- `--chunk_length`: Chunk length. |
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- `--input_length`: Input length. |
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- `--model`: Model to use, default is `gpt-4o-mini-2024-07-18`. |
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- `--api_url`: Your API URL, default is os.getenv("OPENAI_BASE_URL"). |
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- `--api_key`: Your API Key, default is os.getenv("OPENAI_API_KEY"). |
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- `--num_workers`: Number of workers, each worker will process one example. |
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You can also set the environment variables `OPENAI_BASE_URL` and `OPENAI_API_KEY` to avoid typing them in the command line. |
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```bash |
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export OPENAI_BASE_URL="YOUR_API_URL" |
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export OPENAI_API_KEY="YOUR_API_KEY" |
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``` |
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### Evaluation |
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We provide a script to evaluate the generated predictions. For RAG task, the evaluation is based on the [HotpotQA](https://github.com/hotpotqa/hotpot). For En.QA and Zh.QA task, the evaluation is based on the [InfiniteBench](https://github.com/OpenBMB/InfiniteBench). |
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For RAG task: |
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```bash |
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bash scripts/eval_rag.sh /path/to/your/output_dir |
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``` |
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For En.QA task: |
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```bash |
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bash scripts/eval_en.sh /path/to/your/output_dir |
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``` |
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For Zh.QA task: |
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```bash |
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bash scripts/eval_zh.sh /path/to/your/output_dir |
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``` |
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## Citation |
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If you find this project helpful, please cite it as follows: |
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```bibtex |
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@article{liu2025extagents, |
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title={Scaling External Knowledge Input Beyond The Context Length of LLMs via Multi-Agent Collaboration}, |
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author={Zijun Liu and Zhennan Wan and Peng Li and Ming Yan and Ji Zhang and Fei Huang and Yang Liu}, |
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year={2025} |
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} |
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``` |
